Scientists discover new AI phenomenon – “indoor training effect”
Researchers from Massachusetts Institute of Technology (MIT) and other scientific centers made an unexpected discovery in AI training that contradicts conventional approaches to training AI agents.
Scientists discovered a phenomenon they named the “indoor training effect”. Contrary to the traditional view that simulated training environments should precisely match real operating conditions, the research showed: training in a completely different, more predictable environment can lead to better results.
“If we learn to play tennis indoors where there’s no noise, we can more easily master different shots. Then, moving to a noisier environment, like a windy court, we might have a higher chance of playing well than if we had started training in windy conditions,” explains Serena Bono, MIT Media Lab researcher and lead author of the study.
To test their theory, researchers used Atari games modified to include an element of unpredictability. Specifically, they experimented with Pac-Man, altering ghost movement probabilities. The results were unexpected: an AI agent trained in a noise-free version of the game showed better results in a “noisy” environment than an agent trained with interference.
This discovery is particularly important for home robotics development. Traditionally, it was thought that a robot trained to perform household tasks in a factory might work inefficiently in a user’s kitchen due to environmental differences. The new research offers a fundamentally different approach to solving this problem.
“This is a completely new perspective on the problem. Instead of trying to make the training environment as similar as possible to the test environment, we can create simulated environments where the AI agent learns even better,” notes study co-author Spandan Madan, a Harvard University graduate student.
Autor: AIvengo
For 5 years I have been working with machine learning and artificial intelligence. And this field never ceases to amaze, inspire and interest me.
Dongfeng deploys 1.7m tall Walker S robots with 41 servosDongfeng Motor joins forces with Ubtech Robotics to integrate innovative Walker S robots into production lines. These technological marvels standing 1 meter and 70 centimeters tall are ready to transform traditional automobile assembly processes. Dongfeng Motor's general manager emphasizes that implementing artificial intelligence in these robots will significantly improve the quality of component inspection and assembly.
MIT graduate student reduced painting restoration from 230 to 3.5 hoursMIT graduate student Alex Kachkin developed a cool method for painting restoration using artificial intelligence. Reducing work time from many months to several hours. As a demonstration, he restored a work by an unknown Dutch master of the 15th century that had seriously suffered from time.
AI prosthetic from Canada analyzes objects and decides how to grasp themArtificial intelligence gives prosthetics independence! Scientists from Memorial University of Newfoundland created a revolutionary arm prosthetic that literally "thinks" for itself. Unlike traditional models that require reading muscle signals through sensors, the new device is completely autonomous.
DeepSeek packed LLM engine into 1200 lines of Python codeThe DeepSeek team presented nano-vLLM. This is a lightweight and compact engine for running large language models. Which could change perceptions about code efficiency. Amazingly, all functionality fit into just 1200 lines of Python code! This is true technological minimalism in the world of artificial intelligence. Traditional engines like this, for all their power, often suffer from an overloaded codebase. Which makes their modification a real trial for developers. Nano-vLLM solves this problem by offering a simple but powerful tool without unnecessary complexity. The code is open.
Tesla robotaxi failure: 11 traffic violations in first days from 20 carsThe dream of robotaxis faces harsh reality! Tesla launched public tests of autonomous taxis in Austin, but the results were far from the promised technological miracle. In the first days of testing, at least 11 serious traffic violations were recorded. And this with only 20 vehicles selected for a limited circle of bloggers. Philip Koopman, professor at Carnegie Mellon University and expert on autonomous technologies, doesn't hide his surprise: "This is terribly fast for so many videos with unstable driving to appear".
